metrics

This is the last post in the series about machine learning in practice. This time the post will be about productionizing machine learning models. I want to share my experience from several production machine learning systems and show how it…

This post is about how to snapshot your model based on custom validation metrics. First we define the custom metric, as shown here. In this case we use the AUC score: import tensorflow as tf from sklearn.metrics import roc_auc_score def…

Keras offers some basic metrics to validate the test data set like accuracy, binary accuracy or categorical accuracy. However, sometimes other metrics are more feasable to evaluate your model. In this post I will show three different approaches to apply…